Empirical mode decomposition with least square support vector machine model for river flow forecasting

Accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. However, single models may not be suitable to capture the nonlinear and non-stationary nature of t...

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Main Author:	Ismail, Shuhaida
Format:	Thesis
Language:	English
Published:	2016
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/77916/1/ShuhaidaIsmailPFS2016.pdf
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spelling	my-utm-ep.779162018-07-18T07:36:37Z Empirical mode decomposition with least square support vector machine model for river flow forecasting 2016-03 Ismail, Shuhaida QA Mathematics Accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. However, single models may not be suitable to capture the nonlinear and non-stationary nature of the data. In this study, a three-step-prediction method based on Empirical Mode Decomposition (EMD), Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) model, referred to as EMD-KPCA-LSSVM is introduced. EMD is used to decompose the river flow data into several Intrinsic Mode Functions (IMFs) and residue. Then, KPCA is used to reduce the dimensionality of the dataset, which are then input into LSSVM for forecasting purposes. This study also presents comparison between the proposed model of EMD-KPCA-LSSVM with EMD-PCA-LSSVM, EMD-LSSVM, Benchmark EMD-LSSVM model proposed by previous researchers and few other benchmark models such as Single LSSVM and Support Vector Machine (SVM) model, EMD-SVM, PCA-LSSVM, and PCA-SVM. These models are ranked based on five statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( r ), Correlation of Efficiency (CE) and Mean Absolute Percentage Error (MAPE). Then, the best ranked model is measured using Mean of Forecasting Error (MFE) to determine its under and over-predicted forecast rate. The results show that EMD-KPCA-LSSVM ranked first based on five measures for Muda, Selangor and Tualang Rivers. This model also indicates a small percentage of under-predicted values compared to the observed river flow values of 1.36%, 0.66%, 4.8% and 2.32% for Muda, Bernam, Selangor and Tualang Rivers, respectively. The study concludes by recommending the application of an EMD-based combined model particularly with kernel-based dimension reduction approach for river flow forecasting due to better prediction results and stability than those achieved from single models. 2016-03 Thesis http://eprints.utm.my/id/eprint/77916/ http://eprints.utm.my/id/eprint/77916/1/ShuhaidaIsmailPFS2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97580 phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Ismail, Shuhaida Empirical mode decomposition with least square support vector machine model for river flow forecasting
description	Accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. However, single models may not be suitable to capture the nonlinear and non-stationary nature of the data. In this study, a three-step-prediction method based on Empirical Mode Decomposition (EMD), Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) model, referred to as EMD-KPCA-LSSVM is introduced. EMD is used to decompose the river flow data into several Intrinsic Mode Functions (IMFs) and residue. Then, KPCA is used to reduce the dimensionality of the dataset, which are then input into LSSVM for forecasting purposes. This study also presents comparison between the proposed model of EMD-KPCA-LSSVM with EMD-PCA-LSSVM, EMD-LSSVM, Benchmark EMD-LSSVM model proposed by previous researchers and few other benchmark models such as Single LSSVM and Support Vector Machine (SVM) model, EMD-SVM, PCA-LSSVM, and PCA-SVM. These models are ranked based on five statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( r ), Correlation of Efficiency (CE) and Mean Absolute Percentage Error (MAPE). Then, the best ranked model is measured using Mean of Forecasting Error (MFE) to determine its under and over-predicted forecast rate. The results show that EMD-KPCA-LSSVM ranked first based on five measures for Muda, Selangor and Tualang Rivers. This model also indicates a small percentage of under-predicted values compared to the observed river flow values of 1.36%, 0.66%, 4.8% and 2.32% for Muda, Bernam, Selangor and Tualang Rivers, respectively. The study concludes by recommending the application of an EMD-based combined model particularly with kernel-based dimension reduction approach for river flow forecasting due to better prediction results and stability than those achieved from single models.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ismail, Shuhaida
author_facet	Ismail, Shuhaida
author_sort	Ismail, Shuhaida
title	Empirical mode decomposition with least square support vector machine model for river flow forecasting
title_short	Empirical mode decomposition with least square support vector machine model for river flow forecasting
title_full	Empirical mode decomposition with least square support vector machine model for river flow forecasting
title_fullStr	Empirical mode decomposition with least square support vector machine model for river flow forecasting
title_full_unstemmed	Empirical mode decomposition with least square support vector machine model for river flow forecasting
title_sort	empirical mode decomposition with least square support vector machine model for river flow forecasting
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2016
url	http://eprints.utm.my/id/eprint/77916/1/ShuhaidaIsmailPFS2016.pdf
_version_	1747817862231228416

Empirical mode decomposition with least square support vector machine model for river flow forecasting

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